Effects of FeSO₄ and Organic Additives on Soil Properties and Microbiota during Model Soybean Planting in Saline-Alkali Soil

Abstract

Saline soils are characterized by organic matter and nutrient deficiencies, and their mineral fraction consists almost exclusively of fine sand particles, resulting in an unstable soil formation process. Due to the high amount of soluble salts in the soil, the osmotic pressure of the soil is elevated, restricting water absorption. This ultimately leads to the death of the plant and adversely impacts crop growth and yield. Incorporating Fe²⁺ can improve fertilizer utilization efficiency by reducing the oxidation of NH₄⁺ to nitrogen (N₂). However, reports on the usage of iron addition for the improvement of saline-alkali soils are scanty. This study conducted an outdoor simulation in pots to assess the soils of soybean crops during the podding stage. The effects of Fe²⁺ along with organic fertilizer or bio-C addition were elucidated on the composition and function of saline and alkaline microbial communities. The findings were correlated with soil environmental factors to analyze the dynamic changes in soil microbial communities. The soil pH decreased by 1.22–2.18% and SOM increased by 2.87–11.77% with organic fertilizer (OF) treatment. Compared to the ck treatment (control without iron supplementation), other treatments showed an average increase in abundance of dominant phylum by 8.25–11.23%, and an increase in the diversity and richness of the microbial community by 1.73–10.87%. The harmful bacteria in the Actinobacteriota, Chloroflexi, and Basidiomycota groups reduced by 57.83%, 74.29%, and 67.29%, and the beneficial bacteria in Ascomycota increased by 18.23–20.39%. Fe²⁺ combined with organic fertilizer or bio-C treatment could weaken the competitive relationship between the various bacterial lineages, enhance synergistic ability, favor the function and structure of the microbial community, and thus, improve the soil environment. Overall, the application of Fe²⁺ combined with organic fertilizers improved the saline-alkali soil, while the biochar (C) treatment mainly affected the soil nutrients. Through its detailed analysis, the study provides actionable insights for farmers to manage soil fertility in saline-alkaline soils, thereby overcoming the challenges of poor yields due to salinity stress. This will lead to resilient and sustainable farming systems, contributing to global food security.

1. Introduction

Saline-alkaline soils significantly impact vast arable lands, accounting for ~1125 million hectares globally [1]. China has ~3.7 × 10⁷ hectares of such soils, primarily concentrated in its Northeastern plain, central–northern region, and coastal areas [2]. Soybean (Glycine max L.) is crucial for protein and oil production but has limited growth potential in saline-alkali soils. Thus, despite China ranking fourth in global soybean production, its aim to reduce import dependency requires addressing the challenges of the soil [3]. The main methods of land modification include: (1) physical methods, such as improved irrigation, altering water-salt dynamics, reducing evaporation and aiding salt leaching [4,5]; and (2) biological strategies, like cultivating salt-tolerant plants and deploying microbial techniques, contributing to soil improvement and fertility.

Introducing iron ions into these soils holds promise as iron influences soil pH, nutrient availability, and organic matter decomposition, potentially improving soybean growth [6,7]. However, there has been limited study of the combined effects of iron ion supplementation on soybean cultivation in saline-alkali soils. Previous studies have reported that ferric iron (Fe³⁺) aids in sodium removal through precipitation, reducing soil salinity and creating a conducive environment for plant roots. Iron ions’ effects are contingent upon soil characteristics, pH, and their specific forms and concentrations [8]. However, excessive iron concentrations can disrupt microbial processes, impacting nutrient availability and organic matter decomposition. Iron also influences rhizosphere microorganisms, affecting plant–microbe interactions and nutrient cycling [9]. Furthermore, sodium sulfate in saline-alkali soil can be activated by ferrous ions (Fe²⁺) to produce a strong oxidant called sulfate radical (SO₄⁻), with a redox potential of 2.6 V, which can potentially destroy the organic pollutants in the soil [10]. In soybean, niacinamide (nicotinamide, NA), a secondary metabolism secreted by the root, chelates the free Fe²⁺ and then transports the chelates to the cells by an iron transporter [11]. Based on the above two points, ferrous sulfate was added in this study to offset the pollution risk caused by the addition of organic fertilizers. Therefore, understanding this interplay is critical for sustainable soil management and ecosystem balance. Thus, considering parameters like nutrient composition and local conditions is crucial when selecting these substances [12].

Moreover, organic material suppresses soilborne diseases, reduces the reliance on synthetic fertilizers and pesticides, and minimizes the need for expensive soil remediation strategies [13,14]. Biochar’s porous nature enhances water retention, nutrient adsorption, and crop yields. It also aids in carbon sequestration, which can provide economic benefits through carbon credit trading or incentives [15]. The results showed that the height, root length, root crown ratio, and dry quality increased by 12.61%, 191.49%, 42.93%, and 100.00%, respectively. The formaldehyde and reactive oxygen content decreased by 65.76% and 46.46%, respectively. The peroxide, SOD, chlorophyll, and soluble protein increased by 117.35%, 44.75%, 55.00% and 19.31%, respectively [16]. However, studies on the combination of iron ions and organic fertilizers are lacking. The biochar and pyroligneous acid application is anticipated to further improve soil’s physical and chemical characteristics, influencing factors like water retention, pH levels, and nutrient concentrations [17].

These amendments enhance soil structure, water retention, and root development, leading to increased nutrient assimilation by soybean plants and consequently improved photosynthetic activity [18]. Pyroligneous acid lowers salinity-induced stress, positively impacting water absorption and ion uptake, crucial for photosynthesis in soybean crops [19]. Furthermore, biochar, derived from organic matter, enhances photosynthetic rates and water use efficiency in various crops, including peanuts and soybeans, by elevating leaf photosynthetic rates, chlorophyll content, and nitrogen balance index [20]. This organic material also contains growth-promoting compounds like auxins and cytokinins that favorably impact soybean growth and photosynthesis.

In terms of soil microbial diversity, the addition of organic material also augments microbial biomass and biodiversity. Manure improves soil structure, aiding aeration, water infiltration, and root penetration, promoting a flourishing microbial population [21]. Its phenolic compounds greatly impact the microbial degradation of organic substances and nutrient cycling, necessitating precise dosage and application for optimal effects. Similarly, organic acid increases microbial diversity, enhancing soil functioning, resilience, and health [22]. Based on the above, it could be speculated that the reasonable combination of FeSO₄ with organic fertilizer or biological C can fully improve the saline-alkali soil. This could be of great significance for maintaining the coordination of water and fertilizers, the nutrient balance and sustainable utilization of saline-alkali land, as well as the ecological reconstruction of saline-alkali soil. Thus, the present work evaluated the impact of the integration of organic material and iron ions on soybean growth in saline-alkali soils, specifically focusing on crop yield, economic benefits and the behavior of soil microorganisms.

2. Materials and Methods

2.1. Field Site

This experiment was conducted from April to October 2021 at the experimental base of the College of Agriculture, Yanbian University, located in Yanji City, Yanbian Korean Autonomous Prefecture, Jilin Province (42°92′ E, 129°49′ N). This location lies within a high-latitude basin in the middle temperate zone, characterized by a temperate monsoon climate. The average annual precipitation recorded was 583 mm, while the average annual temperature was 7.1 °C. The lowest temperature observed was −31.4 °C, and the frost-free period extended over 146 days throughout the year. To model the field environment, the experiment was conducted in pots placed outdoors., The potting soil was taken from the typical saline soil of Baicheng City, Jilin Province. The pots had a surface area of 0.066 m², a volume of 0.015 cm³ and a saline-alkaline soil layer measuring 30 cm in thickness.

Table 1. Initial characteristics of this soil.

	pH	EC uS cm−1	SOM g kg−1	TN g kg−1	TP g kg−1	TK g kg−1	TF mg kg−1
content	8.52	59.51	59.24	0.33	0.42	2.17	95.24

Note: soil conductivity (EC), organic matter (SOM), total nitrogen (TN), total phosphorus (TP), total potassium (TK) and total iron (TF).

outlines the initial attributes of these soils. A total of 9 groups of treatments were designed in this experiment. Each group of treatments was set up with the same 3 pot-planting trials. The soybean test variety was Hefeng 55, which was provided by the Heilongjiang Provincial College of Agricultural Science. Six plants were planted in each pot, and three inconsistent soybean plants were pulled out at the seedling stage. This ensured the field planting density of 20.0–22.0 thousand plants per hectare. The bottom fertilizer used in this experiment was consistent with the amount of fertilizer applied by farmers in the area. This comprised 60 kg ha⁻¹ of total nitrogen, and additional phosphorus (P₂O₅) and potassium (K₂O) fertilizers added at 75 kg ha⁻¹. Figure 1 details the experimental planting distribution.

2.2. Experimental Design

Of the nine groups of treatments in this experiment, FeSO₄ (powder, 98%, DEWODUO, Shijiazhuang, China) was applied to the eight groups of treatments, with only the bottom fertilizer serving as the control treatment (ck). The application rate for FeSO₄ was determined based on an Fe/soil ratio of 15:1000. On this basis, the experiment was set up with four gradients (0.5, 1, 2, 5 t/1000 m²) of added organic fertilizer treatments and three gradients (C/soil ratio of 26:1000, 52:1000, 104:1000) of added biochar (water-powdered type, DEWODUO, HeBei, China) treatments. Organic fertilizer was kindly provided by the Yanbian University Practice Teaching Base. It was prepared by mixing Chinese medicine residue (Pueraria Mirifica and Ginseng residue) with pig manure. The carbon and nitrogen ratio was adjusted to 25:1, while the moisture content was set at 60%. All of the above fertilizers were mixed with the bottom fertilizer and applied evenly to the soil. Specific fertilization measures and experimental groups are shown in

Table 2. Fertilization measures and experimental groups.

Sample Amendments	Fertilization Measures
ck	60 N kg ha−1; 75 P kg ha−1; 75 K kg ha−1
Fe	60 N kg ha−1; 75 P kg ha−1; 75 K kg ha−1; FeSO4: 15 g kg−1
OF0.5	60 N kg ha−1; 75 P kg ha−1; 75 K kg ha−1; FeSO4: 15 g kg−1; organic: 0.5 t/1000 m2
OF1	60 N kg ha−1; 75 P kg ha−1; 75 K kg ha−1; FeSO4: 15 g kg−1; organic: 1 t/1000 m2
OF2	60 N kg ha−1; 75 P kg ha−1; 75 K kg ha−1; FeSO4: 15 g kg−1; organic: 2 t/1000 m2
OF5	60 N kg ha−1; 75 P kg ha−1; 75 K kg ha−1; FeSO4: 15 g kg−1; organic: 5 t/1000 m2
C0.5	60 N kg ha−1; 75 P kg ha−1; 75 K kg ha−1; FeSO4: 15 g kg−1; Bio-charcoal: 26 g kg−1
C1	60 N kg ha−1; 75 P kg ha−1; 75 K kg ha−1; FeSO4: 15 g kg−1; Bio-charcoal: 52 g kg−1
C2	60 N kg ha−1; 75 P kg ha−1; 75 K kg ha−1; FeSO4: 15 g kg−1; Bio-charcoal: 104 g kg−1

Note: nitrogen (N), P₂O₅ (P), K₂O (K).

, in which the original pH value of the organic fertilizer was 7.93.

2.3. Soil Sampling and Analysis

Soil sampling was carried out on 1 October 2021, using the soil auger method. The root samples were taken from 0 to 20 cm soil depth at five points per pot plant. Fresh soil samples were collected, mixed thoroughly and placed in an ice box. Impurities such as stones and plant residues were removed. A portion of the soil samples were air-dried and used for chemical analyses. The other portion was stored in a refrigerator at −80 °C for microbial biomass determination.

Soil pH [23] and EC [24] were extracted according to the water/soil ratio of 5:1, shaken at 180 r/min for 5 min, and left to stand for 30 min. A pH meter (pH-100A, 100–2000 rpm, LICHEN, Shanghai, China) and a conductivity meter (DDSJ-11A-307, YUEPING, Shanghai, China), were used to determine pH and EC, respectively. Ammonium nitrogen (NH₄-N) and nitrate nitrogen (NO₃-N) were extracted with 2 mol/L CaCl₂, shaken at 180 r/min for 60 min, rested for 30 min, and determined by AA3 continuous flow analyzer [25]. Soil fast-acting phosphorus (AP) fractions were extracted with sodium bicarbonate, shaken at 180 r/min for 2 h, left for 30 min, and filtered through a 0.45 µm PES filter membrane. Soil total nitrogen (TN) and total phosphorus (TP) were digested by Kjeldahl digestion and perchloric acid-sulfuric acid digestion, respectively. The obtained samples were filtered through 0.45 µm PES membranes, and subjected to a continuous flow analyzer (AA3, AutoAnalyzer 3, Technicon, Windows/NT) [26]. Soil total potassium (TK) was digested by perchloric acid-sulfuric acid digestion and filtered through a 0.45 µm PES filter membrane. Soil fast-acting potassium (AK) fractions were extracted with ammonium acetate, shaken at 180 r/min for 2 h, left to stand for 30 min, and filtered through a 0.45 µm PES membrane. The prepared samples were evaluated by flame photometric method (FP6400, INESA, Shanghai, China). The volumetric method with potassium dichromate and dilution calorimetry was used to assess SOM fractions [27].

Soil Fe²⁺ was determined by the o-phenanthroline colorimetric method [28]. The ferrous iron in the soil samples was extracted with water, and the Fe²⁺ in the leachate reacted with o-phenanthroline to form a stable orange-red complex within the range of pH 2–9. This complex concentration was determined by colorimetric method at a wavelength of 530 nm using a spectrophotometer. The content of iron in soil Fe³⁺ was determined by atomic absorption spectrophotometry (AAS) using the characteristic spectrum lines of iron emitted by an iron hollow cathode lamp. The energy absorbed by ground-state iron atoms in the flame was measured through the air-acetylene flame containing iron in the mist. The total iron content of the soil was the sum of Fe²⁺ and Fe³⁺ contents [3].

2.4. Soil Microbial Community Analysis

The DNA was extracted from soil samples using the DNA extraction kit (MN NudeoSpin 96 Soi) (SoilMaster, Shanghai, China) and quantified with a NanoDrop 2000 (Scientific, Beijing, China). PCR reaction conditions were as follows: pre-denaturation at 94 °C for 5 min; 30 cycles of 94 °C for 30 s, 50 °C for 30 s, and 72 °C for 60 s; followed by a stable extension at 72 °C for 7 min and final storage at 4 °C. The quality of DNA extraction was assessed by 1% agarose gel electrophoresis. To amplify the V3-V4 region, bacterial 16S primers were employed, i.e., 338F (5′-ACTCCTACGGGAGGCAGCAG-3′) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′). For 18S, ITS1F (5′-CTTGGTCATTTAGAGGAAGTAA-3′) and ITS2 (5′-GCTGCGTTTCTTTCATCGATGC-3′) were used to amplify the ITS1 region. The products were purified, quantified, and standardized to construct sequencing libraries of 2 × 300 PE for 16S and 2 × 250 PE for 18S. The constructed libraries were subjected to library quality control and the qualified libraries were sequenced using Illumina Novaseq 6000 [29], BioProjects registration number MJ20220103024-MJ-M-20220103061 (https://cloud.majorbio.com).

2.5. Statistical Analysis

SPSS 22.0 (https://spss.en.softonic.com/?ex=RAMP-1768.0, accessed on 14 May 2024), an IBM software (rsa 9.5) was used for statistical analyses. One-way analysis of variance (ANOVA) was used to compare the effects of crop rotation on soil parameters. After evaluating significant differences between the sample means via one-way ANOVA, the Duncan test, a post hoc test was used to validate which specific group means were critically different. The FDR (false discovery rate) for p-values was stringently regulated to ensure minimal false positives. Pearson’s correlation test was performed to evaluate the relationships between the relative abundance of dominant microorganisms, and soil chemical properties. Beta diversity analysis was conducted based on the Bray–Curtis coefficient of variation. Principal component analysis (PCA) was chosen to compare the degree of similarity in community diversity between different samples. To examine the interactions between soil microbial communities under varying tillage levels of continuous and rotational crops, a genus-level covariance network model was constructed [20]. The PICRUSt (phylogenetic investigation of communities by reconstruction of unobserved states) function prediction method was used to assess the functions of soil bacteria and fungi under different treatments and screen the expression abundance of nutrient metabolism-related enzyme genes [21].

3. Results

3.1. Soil pH, EC, and Organic Matter

As seen in Figure 2, the soil pH subjected to iron treatment (Fe) increased significantly by 0.94% compared to the ck treatment, whereas the soil organic matter (SOM) content significantly decreased by 2.68% (p < 0.05). On this basis, the soil pH value of the organic fertilizer (OF) treatment showed a decreasing trend with the increase in application amount. Contrarily, the effect of the application of biochar (C) treatment was the opposite, exhibiting an upward trend with the increase in application amount. The pH values of the OF5 and C0.5 treatments were the lowest, dropping by 1.22% and 2.18%, compared to the ck treatment, respectively.

The trend In soil EC value was consistent with the application of the OF and C treatment, showing an Initial increase and then a decrease with the rising application amount. The OF1 and C1 treatments had the highest soil EC value, which increased by 37.42% and 35.78%, respectively, compared to the ck treatment. The soil EC value of the C2 treatment was the lowest, significantly reduced by 5.52–31.24% compared to the other treatments (p < 0.05). The application of organic fertilizer had a better effect on increasing SOM content than the biochar treatment.

The SOM content increased with the rise in OF amount. The highest SOM content was observed in the OF5 treatment, which was significantly higher than the other treatments by 2.87–11.77%. However, the application of biochar had no discernible effect on the increase in SOM content, with C2 treatment significantly reducing SOM content by 2.55% (p < 0.05).

3.2. Nutrients in Soil

The findings presented in Figure 3a–c indicate that the soil total nitrogen (TN) and total potassium (TK) content in the Fe treatment decreased by 10.72% and 7.27% compared to ck treatment, while the soil total phosphorus (TP) content significantly increased by 88.48% (p < 0.05). Furthermore, TN, TP, and TK contents of each treatment were higher than those of the ck treatment, with significant rises of 22.02–31.40%, 26.95–32.87%, and 0.88–12.07%, respectively (p < 0.05). The OF treatment had a better effect on increasing soil TN content than the biochar (C) treatment, and the soil TN content continued to rise with the increase in application amount. The soil TN content for each C treatment decreased by 2.89–5.95% compared to the ck treatment. As compared to the ck treatment, the application of OF and C significantly increased soil TP content, with an increase of 10.32–112.26% and 88.01–103.17%, respectively. However, the TK content was significantly lower, with a reduction of 25.85% (p < 0.05).

The available nutrients in the Fe-treated soil were lower than those in the ck-treated soil (Figure 3d–f). The application of the OF treatment increased the soil NH₄⁺-N content but lowered the soil NO₃⁻-N content. Among them, the OF1 treatment had the highest soil NH₄⁺-N content, which was 6.48% higher than the ck treatment. With the increase in application amount, the soil NH₄⁺-N content exhibited a downward trend. The soil NO₃⁻-N content was reduced by an average of 4.83–80.31% compared to the ck treatment. The impact of C treatment on soil NH₄⁺-N and NO₃⁻-N content was opposite to that of the OF treatment. The application of C raised soil NO₃⁻-N content but reduced NH₄⁺-N content. As compared to the ck treatment, the average soil NH₄⁺-N content declined by 18.59–38.52%. The soil NO₃⁻-N content under the C1 treatment was significantly higher than that under the ck treatment, increasing by 86.83%. The OF treatment can increase the soil AP and AK content, and this trend exhibits a positive association with the increasing application amount. The soil AP and AK contents were the highest in the OF5 treatment, which were significantly higher than the control treatment by 52.99% and 22.41% (p < 0.05). The soil AP and AK contents rose significantly in the C0.5 and C1 treatments, with increases of 16.96% and 15.99%, respectively (p < 0.05).

3.3. Changes in Iron Forms in Soil

The analysis in Figure 4a–c shows that in comparison to the ck treatment, the total Fe and Fe³⁺ in the soil treated with iron (Fe) significantly increased by 25.32% and 169.73%, respectively. However, the soil Fe²⁺ content exhibited a significant reduction of 43.66% (p < 0.05). Furthermore, the total Fe content in soil treated with OF showed an upward trend with the increase in application amount, while the effect of C treatment was different. The C1 treatment had the highest total Fe content in the soil, which grew by 77.23% and 41.43% compared to ck and Fe treatments, respectively.

The OF and C treatment demonstrated a consistent trend in the changes in soil Fe²⁺ and total Fe content, first rising and then falling with the increasing application amounts. Among them, OF5 and C1 treatments resulted in the highest soil Fe²⁺ content, which increased by 18.57% and 4.31%, respectively, compared to the ck treatment. The EC value of the soil was the lowest in the C2 treatment, being significantly reduced by 13.94–45.98% compared to the other treatments (p < 0.05).

The biochar application had a greater impact on increasing soil Fe³⁺ content than the OF treatment. The soil Fe³⁺ content was highest in the C1 treatment, which was significantly higher than other treatments by 1.95–267.03%. The effect of the OF treatment on the rise in soil Fe³⁺ content was 127.03–212.97% (p < 0.05).

3.4. Effect of Adding Organic Material on the Microbial Community of Saline-Alkali Soils

3.4.1. Alpha Diversity Analysis

For exploring the Alpha diversity of the microbial community, the Ace (richness), Chao, and Shannon index (diversity) of the bacteria and fungi were examined in soil samples subjected to different treatments (

Table 3. Statistics of alpha diversity index of saline-alkali soils subjected to different treatments.

Sample	Bacteria			Fungi
	Shannon	Ace	Chao1	Shannon	Ace	Chao1
ck	6.82 b	3948.82 c	3885.41 c	2.98 ab	180.92 b	182.40 b
Fe	6.86 b	4149.32 b	4222.43 b	2.88 b	194.41 a	191.18 a
OF0.5	6.75 bc	4100.96 b	4135.97 b	3.19 ab	159.85 d	158.07 c
OF1	6.89 b	4291.27 ab	4265.37 b	3.29 a	184.80 b	183.56 b
OF2	7.01 a	4503.23 a	4588.71 a	2.99 ab	173.38 c	172.71 b
OF5	6.99 b	4613.84 a	4545.74 a	3.31 a	197.87 a	210.08 a
C0.5	4.54 c	3567.28 d	3582.38 d	2.75 c	197.77 a	198.16 a
C1	6.89 b	4271.97 ab	4304.24 b	3.10 ab	184.52 b	195.50 a
C2	6.83 b	4205.12 b	4204.11 b	3.20 a	173.66 c	178.27 b

Note: The data represent: mean ± SD, n = 9 replicates. The a, b, and c on error line lettering indicate differences between treatments (p < 0.05). The description of each treatment abbreviation for the x-axis is detailed in Table 2.

). There was no significant difference in the Shannon index of bacterial and fungal communities among treatments, but the differences in the Ace and Chao1 indices were more obvious. In comparison to the ck treatment, the bacterial and fungal Ace indices of Fe treatment increased by 5.08% and 7.45%, respectively. With the increase in the amount of organic fertilizer, the Ace indices of the bacterial and fungal communities continued to increase and exhibited a pattern of OF5 > OF2 > OF1 > OF0.5. The bacterial Ace indices of the OF5 treatment increased by 16.84% and 9.27% compared with the ck and Fe treatments. The fungal Ace index increased by 9.37% and 1.78% in the OF5 treatment compared to the ck and Fe treatments, respectively. However, the treatments with the addition of biomass C performed differently, with the highest bacterial Ace index in the C1 treatment, which increased by 8.18% and 1.34% compared to the ck and Fe treatments, respectively. The fungal Ace index, on the other hand, showed a decrease with increasing biomass C content. The C0.5 treatment had the highest Ace index, which increased by 9.31% and 1.73% compared to the ck and Fe treatments, respectively.

In terms of the Chao1 index, the bacterial and fungal Chao1 indices increased by 8.67% and 4.81%, respectively. As the amount of organic fertilizer increased, the Ace index of the fungal community continued to increase, exhibiting a pattern of OF5 > OF2 > OF1 > OF0.5. The fungal Chao1 index of the OF5 treatment increased by 15.18% and 9.89%, respectively, compared with the ck and Fe treatments. The bacterial Ch0.5 treatment had the biggest Ace index, which increased by 9.31% and 1.73%, respectively, compared with the ck and Fe treatments. The bacterial Chao1 index was greatest in the OF2 treatment, increasing by 9.87% and 8.67% compared to ck and Fe treatments, respectively. However, the Chao1 index of the treatments with biomass C addition varied similarly to the Ace index, as reflected by the bacterial Chao1 index, which was highest in the C1 treatment, with an increase of 10.78% and 1.94% over the ck and Fe treatments, respectively. The fungal Ace index, on the other hand, showed a decrease in the Chao1 index with increasing biomass C content, with the C0.5 treatment having the highest Ace index, which increased by 8.64% and 3.65% compared to the ck and Fe treatments, respectively.

3.4.2. PCA of Microbial Communities of Soil Subjected to Different Treatments

The principal component analysis (PCA) based on the Bray–Curtis coefficient of variation was run 999 times and used for evaluating the degree of similarity in different samples in terms of their diversity or community composition. The PC1 elucidated 34.41% and 19.91%, while the PC2 explained 15.38% and 17.90% at the level of bacterial and fungal phyla (97% similarity) for each treatment, respectively. Figure 5a shows a significant influence of the fertilizer treatments on the soil bacterial community at the genus level. The OF2 treatment occurred in the first PC1 associated with positive values. The OF5 treatment seems to be more effective in promoting bacterial diversity, compared to ck treatment with less bacterial diversity. Contrary to this, the Fe treatment found in the PC2 was associated with more negative values. This implied that the OF5 treatment had a more positive impact on bacterial diversity than the Fe treatment. Among the different soil attributes, AK, TN, AP, TP and OM were positively correlated with OF5. The PC1 accounts for the significant variation in data. The positive association between PC1 and YFJ12 shows a significant effect on the bacterial community. In PC2, the correlation was positive for OF5 treatment, suggesting a significant influence on the bacterial biomass but its effect was somewhat different from OF2.

Overall, the PC2 and Fe treatment exhibited a negative correlation and therefore the Fe treatment was observed to be the least effective in bacterial diversity promotion. The variables AK, TN, AP, TP, OM and EC were significant for elaborating the variation in PC1, while pH, TK, NO₃⁻-N and NH₄⁺-N were crucial for PC2. The positive associations were observed among AK with OF5 treatment, while for TN, AP, TP, OM and EC with OF2 and OF0.5 treatments. Contrary to this, a negative correlation was observed for pH, NO₃⁻-N and NH₄⁺-N with Fe treatment. Notably, ck treatment was found in negative correlation for almost all the soil parameters. In summary, the OF2 and OF5 treatments were observed to be the most effective for enhancing soil fertility and microbial diversity, whereas the ck treatment was less beneficial.

The soil fungal communities’ analysis under different fertilizer treatments depicted in Figure 5b showed almost similar responses as those for bacteria. The PC1 observed variation was positively correlated with pH, OM and nitrogen contents, while the PC2 was correlated with soil P, K, etc. The OF2 treatment was positively associated with PC1 for almost all soil parameters, while the ck treatment was negatively correlated with all parameters in PC2. In PC2, OF5 was found to be positively associated, while Fe treatment was negatively correlated with most of the soil parameters. Soil parameters AK and TK were positively correlated with OF5, whereas TN, AP, TP, OM and EC were positively correlated with OF2. Contrary to this, the soil pH, NO₃⁻-N and NH₄⁺-N were negatively correlated with Fe treatment. The TK also exhibited a positive correlation with OF5 treatment.

Overall, fertilizer treatments had a significant influence on soil attributes. Among them, the OF2 treatment had the greatest impact on fungal community and diversity, while the Fe treatment was the least effective.

3.4.3. Changes in Microbial Community Diversity at the Phylum Level

From soil samples subjected to different fertilizer treatments, about 37 phyla of bacteria and 27 phyla of fungi are illustrated in Figure 6. Figure 6a shows the abundance of bacterial community, expressed in percentages, affected by different fertilizer treatments. The dominant phyla of each bacterium under different treatments were consistent, but their abundance varied. The dominant phyla were Proteobacteria, Actinobacteriota, Chloroflexi and Acidobacteriota, whose overall abundance accounted for more than 60% in each treatment. Compared to the ck treatment, the abundance of dominant phyla in the other treatments increased by an average of 8.25–11.23%, with Proteobacteria under the C0.5 treatment being significantly higher than the other treatments, and the other treatments being 134.78–200.15% higher. However, Actinobacteriota and Chloroflexi were reduced by 57.83% and 74.29% compared to ck and Fe treatments. The main difference between the organic fertilizer treatments and ck and Fe was in Proteobacteria abundance, which was higher by 36.42% and 21.52%, respectively. Overall, FeSO₄ with organic fertilizer and bio-C application increased the abundance of bacterial dominant phyla, whereas FeSO₄ with organic fertilizer was more effective in increasing the abundance of Actinobacteriota. Similarly, FeSO₄ with bio-C application was more advantageous in increasing Proteobacteria. For fungi, the dominant phyla under the treatments were consistent, but there were differences in their abundance. The overall abundance of the dominant phyla, i.e., Ascomycota and Basidiomycota, accounted for more than 80% in all the treatments. Compared with the ck treatment, the abundance of the dominant phyla in the other treatments increased by an average of 10.25–20.39%, to which Ascomycota contributed the most. Ascomycota had the highest abundance in the OF2 and C0.5 treatments, which was 18.23% and 20.39% higher than that of the ck treatment, respectively. However, Basidiomycota had the lowest abundance under OF2 and C0.5 treatments, which was still 67.29% and 65.41% higher than the ck treatment, respectively (Figure 6b). Overall, the application of FeSO₄ in combination with organic fertilizer and bio-C application increased the abundance of the dominant fungal phylum, but the relative abundance of Basidiomycota decreased.

3.4.4. Changes in Microbial Community Diversity at the Genus Level

The histogram (Figure 7) shows the genus-level diversity and distribution of microbial communities that were detected in the soil samples subjected to different fertilizer treatments. Figure 7a depicts the relative abundance of the bacterial community on the genus level influenced by varying treatments. Among the different genera of the bacteria, the most abundant genus was observed for Bradyrhizobium followed by norank JG30-KI-CM45, Solirubrobacter, norank-f-norank-o-Vicinamihacterales, norank-f-norank-o-Rokubacteriales, norank-Vicinamihacreraccac, Allorhizobium-Neorhizobium-Pararhizobium-Rhizobium, Arthrobacter, norank-f-norank-o-norank-c-Gitt-GS-136, Massilia, and norank-Gemmatimonadaceae. The ck and Fe treatments were found the most favorable for bacterial growth. On the other hand, OF0.5 and OF1 treatments were observed most conducive for norank-f-JG30-KI-CM45, norank-f-norank-o-Vicinamihacterales, norank-f-norank-oRokubacteriales, norank-Vicinamihacreraccac and norank-f-noranko-norank-c-Gitt-GS-136 bacterial genus growth. The different fertilizer measures resulted in a significant influence on the bacterial community abundance at the genus level. The ck treatment was suitable for the growth of the bacterial genus Bradhyrhizobium. Also, the norank-JG30-KI-CM45 was more abundant in the soil where the OF0.5 and OF1 treatments were applied.

The fungal microbial community diversity at the genus level was significantly affected by different fertilizer treatments (Figure 7b). The ck treatment resulted in the abundance of Cladosporium, norank_p_Zoopagocota, and norank_p_Chytridiomycota. The Fe treatment resulted in a reduction in Cladosporium, norank_p_Zopagocota,norank_p_Chytridiocota, Chaetomium, and unclassified Sordariocetes. The OF0.5, OF1, OF2, and OF5 treatments also resulted in insignificant changes in fungal microbial diversity viz. Chaetomium, unclassified Sordariocetes, Fusarium, Mrakta, Auricularia, and Boeremia. The C1 and C2 treatments resulted in a higher relative abundance of fungal genera unclassified p-Mucorocota and unclassified_k_fungi.

3.4.5. Correlation Analysis between Soil Microbial Community and Soil Environmental Factors

Figure 8 depicts the correlation heatmap for genus-level diversity in the rootzone soil microbial communities for the respective treatments applied. The soil bacterial community correlation heatmap at the genus level exhibited different responses with soil physiochemical attributes as indicated in Figure 8a. This Spearman correlation heatmap expressed the correlation coefficients between the soil attributes and genus-level diversity of bacterial communities in the saline soil rhizosphere, as influenced by different fertilizer measures. Certain bacterial genera were more sensitive to soil physiochemical properties than others. A strong and positive correlation was observed for the bacterial genus Bradyrhizobium with TP and AP, Rubrobactor with TN, Arthrobactor with pH, norank_f_norank_o_norank_c_KD$-96 with TK. Similarly, the bacterial genera Sphingomonas, Bacillus, norank_f_JG30-KF-CM45 and norank_f_Gemmatimonadaceae showed a modest positive correlation with soil pH, NO₃⁻-N, TK and NH₄⁺-N, a slight positive correlation with EC, OM, and AK but negative correlation with other soil properties.

Figure 8b shows that the fungus unclassified_c_Leotiomycetes was strongly and positively correlated with AK, AP and TP, slightly positively correlated with other soil physiochemical attributes and properties, while negatively correlated with pH, NO₃⁻-N and TK. The fungus community genera norank_f_Pyronemataceae was associated positively with all the soil properties except TP which had a strong negative correlation, and NO₃⁻-N which had a slight negative correlation. The norank_p_Mucoromycota showed a strong positive correlation with TK, TN, and NH₄⁺-N, no correlation with AK and slightly negative for the rest. The fungus Mrakia and Thelebolus showed a strong positive correlation for TK, TN and NH₄⁺-N while exhibiting a significantly strong negative correlation for TP. A slight positive correlation of fungus Chaetomium was observed for AP, pH and TP while a negative correlation was observed for the rest of the soil properties. The fungus Fusarium showed a strong correlation with TP and AP followed by a slight positive with the rest of the soil attributes and a negative correlation was observed with NO₃⁻-N, pH, AK and EC. Cladosporium exhibited no correlation with soil TN, and a positive correlation with all the soil attributes except AP, NH₄⁺-N and EC, which were all negatively associated. The fungus genera unclassified_o_Hypocreales showed a strong positive correlation with TN, and OM followed by AK, NH₄⁺-N, and TK, and a negative correlation with all remaining soil attributes. The genus Boeremia displayed a strong positive correlation with pH followed by TP and NO₃⁻-N while a negative correlation was noticeable for the rest of the soil properties.

3.4.6. Functional Prediction Analysis of Soil Microbial Communities

The functional prediction analysis method, i.e., PICRUSt (phylogenetic investigation of communities by reconstruction of unobserved states) was used to determine the bacterial and fungi functions from the rhizosphere of saline-alkali soils subjected to different treatments. Figure 9a,b show the bacterial and fungal community functions, respectively, and unravel the variations and distinctions in the metabolic pathways across samples for the prediction of metabolic functions. These functional variations reflect environmental adaptations and fitness, shedding light on how microbial communities respond to different treatments in saline-alkali soil conditions.

3.4.7. Network Analysis of Soil Microbial Correlations

Figure 10a depicts the network analysis of soil microbial correlations, reflecting the complex and interconnected network of microbial community relationships. The microbial taxa are represented by nodes while the correlation between the class and taxa is determined by edges. The strength of correlation between different communities is represented by edge thickness. The network analysis showed that organic material application with iron ion addition had a significant impact on the rhizosphere microbial correlations in saline-alkali soils. The strength of the different classes of microbes was dependent on organic material addition which sped up the growth of a wide range of microbial communities and their interactions. A significant impact of iron addition was observed on microbial correlations but was more complex as it differed from class to class with diverse responses. This clarified that iron ion addition had a significant influence on the microbial taxa in soil and thereby enhanced the interactions between them. With organic amendments, the relative abundance of Proteobacteria, and Alphaproteobacteria significantly increased, while iron addition decreased the relative abundance of Actinobacteria. Of these, the Bacteroidia and Actinobacteria were found to be the most abundant classes. Overall, the network analysis of the bacterial communities in the soil showed that organic amendments had a significant impact on the structure and function of the microbial communities and highlighted the complex interactions and microbial relationships in the soil.

Figure 10b shows the network analysis of fungal correlations in saline-alkali soils subjected to varying treatments. The analysis showed that the application of organic material and iron had a significant impact on soil fungal correlations in saline soil. The relation between the phyla Eukaryota and SAR (Stramenopiles-, Alveolates-, and Rhizaria-related fungi) suggests that these organisms work together and enhance their chances of survival. Similarly, the phylum Nematozoa and Zoopagomycota also survive and work in correlation. Due to highly correlated taxa, network analysis was classified into several clusters. Among them, the largest cluster was dominated by Ascomycota and Basidiomycota. The other ones included Zygomycota and Mucoromycota. The findings revealed that the large clusters were affected by organic material application while the iron ion addition influenced concentrated small clusters.

4. Discussion

4.1. Effect of Adding Organic Material on Physiochemical Properties of Saline-Alkali Soils

The study investigated the impact of applying organic material with and without iron ions to saline-alkali soils, focusing on the soil’s chemical properties. The addition of these materials significantly influenced soil pH, a crucial factor affecting nutrient availability in the rhizosphere. Organic material application, coupled with iron ion integration, resulted in a linear increase in pH from the seedling to the maturity stages of crop growth. This aligns with prior research indicating that organic material and iron ion applications raise soil pH in saline-alkali soil [30]. Although the applied organic fertilizer was alkaline, the soil pH exhibited a decreasing trend, which became more obvious with the increase in organic fertilizer addition. This was due to the acidification by the functional groups released during the oxidation of organic matter to SOM, a phenomenon that can effectively reduce the soil pH [31]. With the application of FeSO₄, the sulfate increases in the soil, which promotes the sulfate process and directly promotes the process of sulfate-reducing bacteria (sulfate-reducing bacteria, SRB) to reduce sulfate (SO₄²⁻) to sulfite (SO₃²⁻), sulfur oxide (S₂O₃²⁻) to sulfide (S²⁻), thus leading to the increase in soil pH [32]. Variations in pH across different fertilizer measures and crop stages were evident, reflecting changing nutrient demands during crop growth. Iron ion addition, alongside organic material, notably affected the rhizosphere pH, favoring nutrient availability for plant uptake [33,34].

EC, serving as an indirect indicator of soil salinity, demonstrated substantial variations due to organic material and iron ion application, particularly peaking during the early stages of crop growth. Fertilizer application in continuous cropping systems can elevate EC, leading to soil salinity issues. Previous research [35] echoes the importance of judicious fertilizer usage to prevent soil salinity, maintain environmental quality, and manage EC for sustainable production. The SOM content varied with crop growth stages due to the decomposition processes of the crop residues and applied organic material. These findings correspond to earlier studies [36], highlighting the influence of organic material on SOM content and its dependence on material choice, application rate, and state.

Soil total nitrogen decreased consistently across all treatments as the crops progressed from seedling to maturity stage. This decline is commonly attributed to increased nutrient uptake by crops over time [37], with earlier stages generally having a surplus of available nutrients in the soil. Organic material application contributes to a more balanced nitrogen profile in the soil [38]. The decline in total nitrogen content across crop growth stages aligns with previous findings, supporting the notion that crop nutrient demands increase as plants develop. In terms of soil phosphorus content, some treatments showcased a significant increasing trend from seedling to maturity stage, while others demonstrated different responses. A previous study [39] highlighted the diverse influence of organic material and iron ions on soil phosphorus content across treatments. Maximum phosphorus content was observed at pod initiation, followed by the maturity stage, showcasing significant differences. This trend suggests a rise in soil phosphorus content with organic material decomposition. Another study [40] emphasized the positive relationship between soil phosphorus content and crop growth stages, highlighting the importance of optimal phosphorus availability in promoting root penetration, fruit/seed setting, and overall crop growth. The potassium content, both total and available from soil, increased consistently across all treatments involving organic material and iron ion application. This trend aligns with the study by Chandra et al. [41], indicating that potassium originates from organic material decomposition; a process that unfolds gradually in the soil. Interestingly, the treatment involving iron ion application exhibited the highest nitrate and ammonium nitrogen levels during the pod initiation stage, indicating an enhancement in soil nitrogen availability. These findings align with the idea [42] that iron ion application improves soil nitrate and ammonium nitrogen contents. Additionally, the research indicated no significant differences in soil nitrate and nitrogen levels across all growth stages of the crop.

4.2. Effect of Adding Iron Ions and Organic Material on Microbial Communities of Saline-Alkali Soils

Iron is an essential nutrient element in life and can participate in numerous biological metabolic processes, such as photosynthesis, respiration, nitrogen fixation, and DNA biosynthesis [43]. To accommodate the need for iron for the growth metabolism in restrictive, low-iron environments, soil microorganisms have developed multiple iron uptake systems to take iron from the environment. It mainly includes the synthesis and utilization of siderophores, transforming the ferric iron to the more bioavailable ferrous form for absorption, reducing the environmental pH to increase the solubility of iron, and using the host ferritin [44]. Sporophores are a class of low-molecular-weight compounds synthesized by microorganisms under the induction of restrictive, low-iron conditions, with a specific high affinity for Fe³⁺. The majority of microorganisms can synthesize siderophores, which can be divided into three types according to different chemical structures: oxamate, catechol salt, and polyhydroxy carboxylic acid [45]. All types of siderophores can be produced in bacteria, whereas fungi synthesize only oxamate-type siderophores. Moreover, siderophores also play an important role in restricting pathogenic microorganisms, as the chelation of iron can inhibit the utilization of iron by pathogens, thereby inhibiting their growth and metabolic activity [46].

The application of organic material and iron ions in saline-alkali soil profoundly impacts the microbial community. Soil microbes play a crucial role in crop root metabolism and soil fertility. Organic material decomposition after soil application is influenced by factors like material type, temperature, and soil fauna [47]. Iron, an essential micronutrient, significantly influences biochemical processes and plant health [48]. Its addition boosts bacterial density and richness by facilitating enzymatic activities crucial for microbial growth and diversity [49]. Iron ion addition acts as a cofactor for enzymes, and enhances iron-dependent enzymatic activity, fostering bacterial community growth and diversity [50,51]. Effective and sustainable nutrient management in agriculture necessitates consideration of specific conditions and mechanisms [52].

PCA findings highlighted that the OF treatment notably promoted microbial diversity, especially bacteria, while the ck treatment showed lower diversity. Iron ion addition exhibited a negative impact on bacterial diversity. Soil characteristics correlated mostly with OF5, showing higher data variability in PCA1. Bacterial community abundance was influenced by both organic material application and iron ion addition [53]. The variation in bacterial phyla abundance could be attributed to nutrient diversity and chemicals from the organic material affecting bacterial growth. The Firmicutes phylum notably benefited from iron supplementation, showcasing a nuanced relationship between fertilizers and bacterial community abundance. Such outcomes underscore the intricate interplay between fertilizers and bacterial phyla dominance under specific conditions, emphasizing the need for such management strategies for better microbial diversity and environmental sustainability [54,55].

The varied abundance of bacterial genera across fertilizer treatments highlights their impact on specific microbial species. For instance, the prevalence of the nitrogen-fixing genus Bradyrhizobium in ck indicated its suitability for this treatment. At the same time, norank-JG30-KI-CM45, norank-Vicinamihacterales, and norank-Gir-GS-136 were more prominent in OF0.5 and OF1 treatments, indicating their responsiveness to these fertilizer compositions [56]. Such targeting of bacterial species by different fertilizers can significantly impact soil nutrient cycles and plant–microbe interactions, crucial for optimizing fertilizer strategies to enhance crop productivity and soil health [57]. Correlation heatmaps examined specific bacterial species’ relationships with soil physicochemical traits. The findings revealed significant positive associations, such as Bradyrhizobium’s connection with TP and AP, signifying its reliance on these nutrients. Conversely, certain genera exhibited negative associations, suggesting their lesser dependence on the considered soil characteristics. These intricate relationships underscore the bacterial populations’ sensitivity to soil conditions, with certain genera being more responsive to particular elements than others. Understanding these linkages is crucial for managing soil health and microbial populations to enhance crop productivity and ecosystem functioning, particularly in saline soil settings [58].

The rarefaction curve effectively evaluated microbial diversity across soil samples. The continuous linear growth of the curve at shallow sequencing depths indicated the ongoing discovery of new OTUs, signifying a diverse microbial community in the soil. However, the plateauing of the curve at greater sequencing depths suggested the identification of most OTUs in the samples, implying limited potential for uncovering novel species [59]. Divergence in species richness among samples was evident from differences in curve patterns, with ck exhibiting the highest species richness and C0.5 the lowest, consistent with findings by Mensah, A. K (2022) [60]. Network analysis unraveled intricate microbial interactions in response to iron ion addition and organic material application in saline-alkali soils. Organic amendments foster a diverse microbial community and enhance interactions. The incorporation of iron results in complex effects on microbial correlations, eliciting varied reactions from different microbial taxa [61,62]. This complexity underscores the significant influence of iron ions on microbial relationships [63].

4.3. Effects of Organic Material Addition on Soil Fungal Communities in Saline-Alkali Soils

The analysis of fungal community density using various indices, such as Ace, Shannon, and Chao, revealed the significant influence of different fertilizer measures and iron ion additions on soil microbial communities [64]. The organic amendments exhibited a substantial positive effect on fungal community richness, as reflected by higher Ace indices. Additionally, distinct fertilizer treatments demonstrated increased species richness for specific classes, suggesting their effectiveness in enhancing richness in particular contexts. The Chao index, reflecting unique species in the fungal community, was notably increased by organic fertilizers, indicating an association with sufficient nutrients for fungal community development [65]. Consistent with these findings, the Shannon indices were higher for fertilizer treatments compared to controls, indicating a more even distribution and greater diversity of fungal species facilitated by fertilizer applications. The findings could be attributed to treatments providing sufficient and suitable nutrients for the proliferation of fungal communities compared to control measures [66].

PCA offered valuable insights into the relationship between different fertilizer measures and soil parameters, indicating enhanced soil physiochemical properties favoring the soil’s fungal communities. These findings elucidate the distinct relationships between fungal communities and soil attributes. This provides a platform for understanding the complex interactions between them, crucial for optimizing fertilizer application approaches in agriculture and soil fertility [67]. Additionally, different fungal phyla responded distinctly to varying fertilizer measures, with Ascomycota reported as the most abundant followed by Basidiomycota across all measures. Specific treatments resulted in a higher abundance of distinct fungal phyla, emphasizing the impact of different fertilizer applications on fungal community diversity. Furthermore, mixed effects on fungal community abundance were observed with Fe and fertilizer measures, dependent on phylum diversity [68]. Distinct fungal genera abundance varied across the treatments, indicating the influence of fertilizer measures on fungal community makeup [2]. These variations are crucial in comprehending the complex interactions between fertilizer applications and soil microbes, essential for customizing soil management techniques in saline-alkaline environments. Additionally, strong correlations between specific fungi and soil characteristics were observed, underscoring their potential influence on ecosystem dynamics and soil management practices [69].

The investigation into the diversity of fungal microbes across distinct soil samples treated with varying organic amendments and iron additions revealed significant insights into microbial interactions and community dynamics. The rarefaction curves indicated sufficient data volume to reflect sample diversity. These reached plateaus at higher levels of species richness in various treatments, hinting at the potential for novel discoveries through additional sequencing in specific treatments [70]. Furthermore, network analysis highlighted the significant impact of interventions on microbial interactions. The findings emphasized complex connections between the soil fungi and the impact of organic matter and iron ions on community dynamics and interactions in saline-alkali soils. Studies have showcased that compost or organic material can increase soil microbial diversity and activity, serving as sources of nutrients and carbon, and aiding in microbial development. Conversely, intricate impacts of iron ions on soil microbial populations, which can be both beneficial and harmful, have been observed. Cooperative interactions among organisms in response to treatments emphasize the importance of such interactions for adaptability and survival under changing environmental conditions [71]. Distinct clusters altering in response to diverse treatments suggest particular fungal communities’ differential reactions to various interventions, highlighting changes in microbial community organization.

4.4. Limitations of the Study

This study elucidated the influence of the distribution of iron ions and organic material on the physical and chemical properties and microbial diversity of soybean soil. The findings also preliminarily proved that the distribution of iron ion and organic material can promote soybean production. However, the physiological and biochemical indexes and soil need to be verified by long-term and systematic tests. In addition, although the present experiment simulated the field environment, differences may arise from the conclusions obtained under large-scale planting, including the interaction between plants, and tillage measures. Therefore, due to the influence of comprehensive factors such as interannual effect and environmental change, the mechanism of activated iron ions and organic material distribution on soybean growth and soil rhizosphere ecology should also be studied and analyzed systematically through field experiments.

5. Conclusions

The results of this study showed that the combination of Fe²⁺ with organic fertilizer and biochar increased the SOM and nutrient contents. Fe²⁺ combined with biochar increased soil pH, but combined with organic fertilizer significantly reduced soil pH. The correlation analysis between the soil chemical properties, soil microbial dominant bacteria, and microbial community structure helped to identify the optimal ways of applying fertilizers to improve saline-alkali soils. Overall, Fe²⁺ combined with organic material addition can improve soil quality by increasing microbial community diversity and balancing soil nutrients. The insights offered into the impact of Fe²⁺ incorporation with organic fertilizers or BioC systems into saline-alkaline soils pave the way forward for improving intensive cultivation practices and achieving environmentally sustainable agricultural development.